Systems and methods for detecting recoating defects during additive manufacturing processes

Abstract

In some embodiments, systems and methods are related to identifying recoating defects based at least in part on imaged light intensities are disclosed. In other embodiments, systems and methods for predicting the formation of part defects during an additive manufacturing process based at least in part on information related to the presence of recoating defects are disclosed.

1 . An additive manufacturing system comprising:
 a build surface;   one or more laser energy sources;   an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface;   a photosensitive detector configured to image at least a portion of the build surface;   a recoater configured to deposit sequential layers of precursor material on the build surface; and   at least one processor configured to perform the steps of:
 obtain images of a plurality of layers of precursor material on the build surface prior to fusing using the photosensitive detector, wherein the plurality of layers include at least one previous layer and a current layer; and 
 predict formation of potential part defects in the current layer prior to fusing based at least in part on the images. 
   
     
     
         2 . The additive manufacturing system of  claim 1 , wherein the at least one processor is configured to provide information related to the images to a trained statistical defect prediction model to predict the formation of the potential part defects in the current layer. 
     
     
         3 . The additive manufacturing system of  claim 2 , wherein the at least one processor is configured to identify recoating defects in the images. 
     
     
         4 . The additive manufacturing system of  claim 3 , wherein the at least one processor is configured to generate binary masks based at least in part on the identified recoating defects, and provide the binary masks to the trained statistical defect prediction model. 
     
     
         5 . The additive manufacturing system of  claim 3 , wherein the at least one processor is configured to identify one or more portions of the image with light intensities greater than a threshold light intensity to identify the recoating defects. 
     
     
         6 . The additive manufacturing system of  claim 5 , wherein the at least one processor is configured to identify contiguous groups of pixels of the images with intensities greater than the threshold intensity and a size greater than a threshold size to identify the recoating defects. 
     
     
         7 . The additive manufacturing system of  claim 3 , wherein the at least one processor is configured to obtain a plurality of fusing energy maps, wherein the fusing energy maps include information related to energy applied to different portions of each layer of the plurality of layers, and wherein the at least one processor is configured to omit identified recoating defects associated with fusing energies less than a threshold energy. 
     
     
         8 . The additive manufacturing system of  claim 1 , wherein the at least one processor is configured to control one or more operations of the additive manufacturing system based at least in part on the predicted formation of the potential part defects. 
     
     
         9 . The additive manufacturing system of  claim 8 , wherein the one or more operations includes at least one selected from scraping and recoating the build surface with the recoater. 
     
     
         10 . The additive manufacturing system of  claim 1 , wherein the at least one processor is configured to output the predicted formation of the potential part defects to a user. 
     
     
         11 . The additive manufacturing system of  claim 1 , wherein the at least one processor is configured to blur the images. 
     
     
         12 . A method for predicting part defects during an additive manufacturing process, the method comprising:
 obtaining images of a plurality of layers of precursor material on a build surface of an additive manufacturing system prior to fusing, wherein the plurality of layers include at least one previous layer and a current layer; and   predicting formation of potential part defects in the current layer prior to fusing based at least in part on the images.   
     
     
         13 . The method of  claim 12 , wherein predicting the formation of the potential part defects includes providing information related to the images to a trained statistical defect prediction model to predict the formation of the potential part defects in the current layer. 
     
     
         14 . The method of  claim 13 , further comprising identifying recoating defects in the images. 
     
     
         15 . The method of  claim 14 , further comprising generating binary masks based at least in part on the identified recoating defects, and providing the binary masks to the trained statistical defect prediction model. 
     
     
         16 . The method of  claim 14 , wherein identifying the recoating defects is based at least in part on identifying one or more portions of the image with light intensities greater than a threshold light intensity. 
     
     
         17 . The method of  claim 16 , wherein identifying the recoating defects includes identifying contiguous groups of pixels of the images with intensities greater than the threshold intensity and a size greater than a threshold size. 
     
     
         18 . The method of  claim 14 , further comprising obtaining a plurality of fusing energy maps, wherein the fusing energy maps include information related to energy applied to different portions of each layer of the plurality of layers, and omitting identified recoating defects associated with fusing energies less than a threshold energy. 
     
     
         19 . The method of  claim 12 , further comprising controlling one or more operations of the additive manufacturing system based at least in part on the predicted formation of the potential part defects. 
     
     
         20 . The method of  claim 19 , wherein the one or more operations includes at least one selected from scraping and recoating the build surface. 
     
     
         21 . The method of  claim 12 , further comprising outputting the predicted formation of the potential part defects to a user. 
     
     
         22 . The method of  claim 12 , further comprising blurring the images. 
     
     
         23 . The method of  claim 12 , further comprising fusing the precursor material with one or more laser energy pixels to form one or more parts on the build surface. 
     
     
         24 . A non-transitory computer readable memory including instructions that when executed by at least one processor performs the method of  claim 12 . 
     
     
         25 . A part manufactured using the method of  claim 12 . 
     
     
         26 . A method of training a recoating defect detection statistical model, the method comprising:
 obtaining training data, wherein the training data includes information related to part defects in a plurality of parts and information associated with images of recoated precursor material layers prior to fusing and associated with forming the plurality of parts;   generating a trained statistical defect prediction model using the training data; and   storing the trained statistical defect prediction model on non-transitory computer readable memory for subsequent use.   
     
     
         27 - 35 . (canceled) 
     
     
         36 . An additive manufacturing system comprising:
 a build surface;   one or more laser energy sources;   an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface;   one or more lights configured to illuminate the build surface;   a photosensitive detector configured to image at least a portion of the build surface;   a recoater configured to deposit sequential layers of precursor material on the build surface; and   at least one processor configured to perform the steps of:
 obtain an image of at least a portion of the current layer prior to fusing the current layer using the photosensitive detector; and 
 identify recoating defects in the current layer based at least in part on light intensities in the image. 
   
     
     
         37 - 42 . (canceled) 
     
     
         43 . A method of detecting recoating defects on a build surface of an additive manufacturing system, the method comprising:
 obtaining an image of at least a portion of the build surface with a recoated precursor layer disposed on the build surface; and   identifying the recoating defects based at least in part on light intensities in the image.   
     
     
         44 - 52 . (canceled) 
     
     
         53 . An additive manufacturing system comprising:
 a build surface;   one or more laser energy sources;   an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface;   a photosensitive detector configured to image at least a portion of the build surface;   a recoater configured to deposit sequential layers of precursor material on the build surface; and   at least one processor configured to perform the steps of:
 obtain one or more fusing energy maps including information related to energy applied to different portions of one or more layers of precursor material on the build surface; 
 obtain one or more images of the one or more layers prior to fusing using the photosensitive detector; and 
 predict formation of potential part defects in a current layer prior to fusing based at least in part on the one or more images and the one or more fusing energy maps. 
   
     
     
         54 - 67 . (canceled) 
     
     
         68 . A method for predicting part defects during an additive manufacturing process, the method comprising:
 obtaining one or more fusing energy maps including information related to energy applied to different portions of one or more layer of precursor material on a build surface of an additive manufacturing system;   obtaining one or more images of the one or more layers prior to fusing using a photosensitive detector; and   predicting formation of potential part defects in a current layer prior to fusing based at least in part on the one or more images and the one or more fusing energy maps.   
     
     
         69 - 85 . (canceled)